99. Emerging Researchers' Group (for presentation at Emerging Researchers' Conference)
Paper
Socioeconomic Status, Parenting Factors and Educational Motivations Among 12-15 Years Old in China: How Do They Relate?
Fang Xu
University of Oxford, United Kingdom
Presenting Author: Xu, Fang
Motivation is widely acknowledged as a strong predictor of academic achievement (Cambria, Eccles & Wigfield, 2012). Educational motivation predicts attitudes toward school, class participation, homework completion, test performance, attendance, and grades. It is particularly salient in high school because educational decisions made during high school are consequential as students position themselves in further education and their specific interest in certain subjects (Dweck & Leggett, 1988; Manganelli, et al., 2021). According to self-determination theory, motivation is able to play an important role in helping students from low-wealth situations break out of poverty by raising certain behaviours to overcome the inequality barrier (Ryan & Deci, 2017). However, previous studies have demonstrated that low-socioeconomic status (SES) students endorse lower levels of achievement motivation than high-SES students (Manganelli, et al., 2021). Nevertheless, some low SES students obtain high educational motivations in China, albeit it “against the odds”, and achieve high academic achievements afterwards (e.g. Xie, 2015). Underlining the cultural background of filial piety in China, Guo and his colleagues (2021) also emphasise the role of parenting factors, including parental control and support, on adolescents’ motivations. Thus, it may be that parenting factors, are especially key for young people from low SES backgrounds to be highly motivated. For example, Leung and Shek (2016) have identified that parent-child discrepancy in perceived parental sacrifice influences the motivation of poor Chinese adolescents based on their study of 275 adolescents and their families in Hong Kong. Therefore, it is worthwhile to explore how the parenting factors associate with the SES-academic motivation relationship in China. Moreover, empirical evidence of how disadvantaged adolescents can be more motivated academically could supplement studies of social justice.
Led by the theoretical framework of Coleman’s social capital theory in the family (Coleman, 1988), this study regards educational motivation as a non-cognitive ability outcome, which is considered to be closely related to future academic achievement (Ames, 1992). Coleman’s theory of social capital in the family suggests that financial capital, human capital and social capital all affect educational outcomes jointly and intercorrelate with each other (Coleman, 1988). Following this framework, this study aims to explore the association of parenting factors, SES and educational motivations among 12 to 15 years old adolescents in China using a nationally representative secondary dataset, the China Education Panel Survey (CEPS). This secondary data was collected from around 20,000 secondary school students from 112 schools in 28 counties in mainland China (National Survey Research Centre at Renmin University of China, 2015). Through conducting quantitative analysis, the article aims to answer the following research questions:
1. What is the educational motivation pattern of adolescents in China? What is the difference in educational motivation patterns between Grade 7 (Age 12 to 13) and Grade 9 (Age 14 to 15) students?
2. How do different kinds of parental capital, including occupation, education and home possessions, relate to students’ educational motivations?
3. How do parental capitals incorporate parenting factors as a form of social capital in the family, including parental involvement, parenting styles and parental aspiration, relate to students’ educational motivation?
This study offers empirical evidence from China to further explain Coleman’s theory of social capital, joining the international theoretical conversation from a different country background. Moreover, it includes a broader range of SES and parenting factors and a wider age range than similar studies that have used large-scale data from China. Furthermore, it explores the patterns of educational motivation of adolescents in mainland China and its relationship with SES and parenting factors, which have not been jointly identified in previous large-scale quantitative educational research.
Methodology, Methods, Research Instruments or Sources UsedThe data of this study is from the 2013-2014 wave (Wave 1) of the China Education Panel Survey (CEPS), including around 10,000 students from Grade 7 and 9,000 students from Grade 9. Regarding the ethical approval, the participants and their parents have provided a written informed consent to the National Survey Research Centre (National Survey Research Centre at Renmin University of China, 2015). The descriptive statistics and correlation matrix have been calculated to understand (1) the differences in forms of parenting factors between families of different SES in China and (2) how the SES and parenting factors correlate with educational motivations. Factor analysis will then be conducted on SES and parenting factors to create the scale for further analysis. To understand the relationship among SES, parenting factors and educational motivations, hierarchical regression models have been completed. All variables have been z-score standardised in the analyses. All the analytical procedure was completed using IBM SPSS Statistics (Version 29) software.
•Dependent Variables: Educational Motivation
Following the measurement of educational motivations from Liu and Chiang (2019), the indicators for educational motivation are students’ self-reported motivations in three main subjects: Chinese, Math and English. The 4-point Likert scale of the three responses (“Chinese/Math/English is highly useful for my future”) ranged from 1 (strongly disagree) to 4 (strongly agree).
•Independent Variables:
1. SES: This research followed previous research (e.g Strand,2014) and create a single composite measure of SES derived from the three variables- parental occupational status, parental educational attainment and home possessions (including family self-reported income, family poverty and family assets).
2. Parenting Factors: Following the conceptualisation of the parenting factors, this research selected 21 parental response items. The parenting style items follow Zhang et al. (2020)’s selection of CEPS indicators of parenting style selection and evaluation. For parental involvement indicators, the research takes into consideration of Hill and Tyson’s (2009) conceptualisation of parental involvement, described previously, and the indicators of parental involvement that Li et al. (2020) adopted in their studies using the CEPS dataset. This research also includes parental educational aspiration as part of parenting factors, which has been widely acknowledged to be a correlated and significant predictor of academic achievement in both the Chinese and Western literature.
•Control Variables: This study’s control variables include hukou, gender, ethnicity, whether the student is an only child and parental absence.
Conclusions, Expected Outcomes or FindingsThe analysis can identify the different motivation patterns for adolescents in different age groups and grades. Considering that those Grade 7 students are freshers for secondary school and Grade 9 students are facing Zhongkao (senior high school entrance exam), choosing whether they would continue to study academically, their educational motivations varied. The study also reveals how parental occupation, parental educational attainment and home possessions are correlated with educational motivations. Moreover, the study also identifies which parenting factor could play a substantial role in the formation of educational motivations. Following Zhang et al. (2020)’s approach using the demandingness and responsiveness scale created by the factor analysis, this research also aims to discover a pattern of the parenting styles’ association with educational motivations. As for the relationship among the SES, parenting factors and educational motivations, the research is expected to claim whether parenting factors can act as mediators or moderators for the SES-educational motivation relationship. In other words, the study is hoping to explore which kind of parenting factors could have positive consequences on motivations for specific SES groups of adolescents. This research can raise important implications for further policy, underlining the importance of certain kinds of parenting factors for educational motivation.
ReferencesAmes, C. (1992) Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84(3), 261-271.
Cambria, Jenna, Eccles, Jacquelynne S, & Wigfield, Allan. (2012). Motivation in Education. In The Oxford Handbook of Human Motivation (Oxford Library of Psychology, The Oxford Handbook of Human Motivation, 2012). Oxford University Press.
Coleman, James S. (1988). Social capital in the creation of human capital. The American Journal of Sociology, 94, S95
Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95, 256-273.
Guo, Mingchun, Wang, Long, Day, Jamin, & Chen, Yanhan. (2021). The Relations of Parental Autonomy Support, Parental Control, and Filial Piety to Chinese Adolescents' Academic Autonomous Motivation: A Mediation Model. Frontiers in Psychology, 12.
Hill, N. E., & Tyson, D. F. (2009). Parental Involvement in Middle School: A Meta-Analytic Assessment of the Strategies That Promote Achievement. Developmental Psychology, 45(3), 740–763.
Leung, Janet T. Y., & Shek, Daniel T. L. (2016). Parent–Child Discrepancies in Perceived Parental Sacrifice and Achievement Motivation of Chinese Adolescents Experiencing Economic Disadvantage. Child Indicators Research, 9(3), 683-700.
Li, X., Yang, H., Wang, H., & Jia, J. (2020). Family socioeconomic status and home-based parental involvement: A mediation analysis of parental attitudes and expectations. Children and Youth Services Review, 116(February)
Liu, Ran, & Chiang, Yi-Lin. (2019). Who is more motivated to learn? The roles of family background and teacher-student interaction in motivating student learning. The Journal of Chinese Sociology, 6(1), 1-17.
Manganelli, Sara, Cavicchiolo, Elisa, Lucidi, Fabio, Galli, Federica, Cozzolino, Mauro, Chirico, Andrea, & Alivernini, Fabio. (2021). Differences and similarities in adolescents' academic motivation across socioeconomic and immigrant backgrounds. Personality and Individual Differences, 182.
National Survey Research Center (NSRC) at Renmin University of China. (2015). China Education Panel Survey [Dataset]. http://ceps.ruc.edu.cn/English/Home.htm
Ryan, R., & Deci, E. (2017). Self-determination theory : Basic psychological needs in motivation, development, and wellness. New York, New York ; London, [England.]
Strand, S. (2014). Ethnicity, gender, social class and achievement gaps at age 16: Intersectionality and “getting it” for the white working class. Research Papers in Education, 29(2), 131–171.
Xie, A. (2015). Inside the College Gate: Rural Students and Their Academic and Social Success.
Zhang, Haochen, Qin, Xuezheng, & Zhou, Jiantao. (2020). Do tiger moms raise superior kids? The impact of parenting style on adolescent human capital formation in China. China Economic Review, 63.
99. Emerging Researchers' Group (for presentation at Emerging Researchers' Conference)
Paper
Understanding Virtual Internationalisation: Perspectives from academic members in higher education
Aysun Caliskan, Zhengwen Qi, Chang Zhu, Ngoc Bich Khuyen Dinh, Yujie Xue
Vrije Universiteit Brussel, Belgium
Presenting Author: Caliskan, Aysun;
Qi, Zhengwen
From the beginning of the 21st century, especially after the outbreak of the COVID-19 pandemic, the digitalization process of higher education has been accelerated worldwide, and the inclusion of virtual dimension in internationalisation actions has also gained momentum in academic practices and discussions (Woicolesco et al., 2022) thereby emerging different definitions and exerting a multifaceted impact on internationalisation practices. While the academia kept monitoring the approaches, rationales, activities or stakeholders of internationalisation of higher education, the most accepted definition of internationalisation of higher education was put forward by J. Knight (2004) as ‘the process of integrating an international, intercultural and global dimension in the purpose, functions, delivery of postsecondary education.’ This definition was later revised and enriched by de Wit et al. (2015), with emphasis on the ‘intentional’ process as well as its benefit for ‘all students and staff’ instead of the mobile few. Based on the Knight’s definition, Bruhn et al., (2020) has proposed to view virtual internationalisation as ‘the process of introducing an international, intercultural, or global dimensions into the delivery, purpose or functions of higher education with the help of ICT’. Recently, Liu (2020) also scrutinized Knight's (2004) definition under Chinese context, and highlighted the Chinese national goal as the purpose and functions of Chinese higher education universities for internationalisation.
In addition to varying definitions of virtual internationalization, the digital age has also embraced changes and brought new possibilities for future internationalisation efforts in many areas (Rajagopal et al., 2020). In terms of virtual course delivery, researchers highlighted the enriched access to unprecedented wealth of online information, tools and services and global knowledge in the digital age, which brought benefits both for education and research (Bruhn et al., 2020; Kobzhev et al., 2020; Moore & Kearsley, 2012; Saykili, 2019). Meanwhile, researchers stressed the addition of an international dimension to educational experiences and more possibilities and forms for internationalisation at home or internationalisation of curriculum in the virtual environment (Bruhn, 2017; de Wit & Hunter, 2015; Kobzhev et al., 2020; Woicolesco et al., 2022). However, as Saykili (2019) quoted, knowledge access and dissemination roles are shifting away from higher education at social level. Amirault & Visser (2010) observed the lack of recognition of virtual internationalisation, which made the virtual mobility programs difficult to benefit from the same advantages as in the offline environment, such as recognition of credits, credits transfer, accreditation, etc.
While previous studies have abundant discussion on the possibilities provided by the digital age for internationalisation, new initiatives continue to emerge and thrive globally, especially after the COVID-19 pandemic. However, relevant studies in the post-pandemic period are still in scarcity. Meanwhile, De Wit & Jones (2022) called for a global cooperative strategy to better understand the multifaceted aspects of internationalisation by involving higher education stakeholders in various contexts. Academic members, as major components of the higher education system, have crucial role for the successful initiation and implementation of virtual internationalisation. This indicates a need to understand the various perceptions of virtual internationalization theoretically and practically from academic members in higher education from various contexts. To provide more up-to-date data for future studies as well as a response to this call, this study aims to explore the perceptions of academic members from a diverse context about virtual internationalization
The research objectives that guided this study are:
(1) What is virtual internationalisation as perceived by academic staff members in higher education?
(2) What are the new possibilities for internationalisation in this digital age as perceived by academic staff members in higher education?
Methodology, Methods, Research Instruments or Sources UsedThe present study applied a qualitative research approach to examine virtual internationalisation from the perspectives of academic staff members in higher education institutions. In this study, focus group was adopted as the research method and eight focus groups (five virtual, three face-to-face) were conducted with 46 participants from five countries. The participants are not randomly sampled as this research was conducted under the framework of an EU Erasmus project. The participants were coming from the project partner institutions and related HEIs. The 8 focus groups consisted of participants from five countries (namely Belgium, Portugal, Austria, Turkey and China). All participants were academic members from higher education institutions, including academic leaders (such as head of research groups, director of research centers, rector/dean/vice dean), teaching staff (professor/associate professor/lecturer), or researchers. Among the focus groups, three were conducted face-to-face; and five were conducted online. Each focus group interview lasted about 50-60 minutes.
All the sessions were audio-recorded with the permission of the participants and later transcribed verbatim. The authors created a coding frame to transform the data into meaningful, manageable, and smaller units as known as codes (Schreier, 2014). This is followed by the processes of structuring main categories and generating the subcategories for each main category (Mayring, 2014). Linked to that, the authors had frequent meetings to discuss the coding frame to analyse the data correctly and comprehensively (Miles, Huberman & Saldana, 1994). They also reached a consensus on themes, sub-themes and codes and resolved any disagreements (Wigginton, Meurk, Ford, & Gartner, 2017).
To ensure the inter-rater reliability, the first two authors read all the transcripts on multiple occasions with a view of performing a content analysis on the data. They also coded the data individually and then checked the extent to which they agree (Erlingsson & Brysiewicz, 2017). This assisted with assessing any potential discrepancies in the coding (of which none were identified) and to develop further codes (Campbell et al., 2013). Additionally, all the authors were involved in this study to reach multiple observations and conclusions so that investigator triangulation could be achieved. This kind of triangulation provided not only confirmation of the findings from different perspectives, but also an in-depth understanding of the phenomenon of interest (Denzin, 1978).
Conclusions, Expected Outcomes or FindingsAs a response to the call of the previous studies (Bruhn, 2017; Kobzhev, et al., 2020; Villar-Onrubia & Rajpal, 2015), this study makes several contributions to understand Virtual Internationalisation in a comprehensive framework. Thus, it conceptualises a framework that includes the various ways in which ICTs can be used for internationalisation of HE. Among those are the conceptualisation of VI and the changes in the digital age. The findings provided empirical evidence on the definition of virtual internationalisation by moving beyond that of Bruhn (2017); Knight (2013) and Kobzhev et al., (2020). In addition to the international, intercultural, and global dimensions in those studies, the results of this study present the importance of the sustainable development as perceived by academic leaders and staff. This comprehensive definition could be used in future research to better assess the possibilities of intersecting internationalisation and ICT. With a deeper investigation of the recent studies (Reimers, 2021), this study presents that higher education institutions in the digital era are playing some crucial roles for the success of international competitiveness and technological development of societies. As digitalisation, and an expansion of flexible distance provision continue to be popular trends, it would be worthwhile further investigating the changes in internationalisation. With its diverse context including countries such as Belgium, Portugal, Austria, Turkey and China, this paper serves as a steppingstone for social inclusion, which the combined forms of course delivery, accessibility to different resources and new cultural environment expand the possibilities for virtual internationalisation.
ReferencesAltbach, P. G., & Knight, J. (2007). The Internationalization of Higher Education: Motivations and Realities. Journal of Studies in International Education, 11(3–4), 290–305.
Amirault, R., & Visser, Y. (2010). The impact of E-learning Programs on the Internationalization of the University. In The Impact of E-Learning Programs on the Internationalization of the University (pp. 1–58).
Bruhn, E. (2017). Towards a Framework for Virtual Internationalization. International Journal of E-Learning & Distance Education, 32(1).
Bruhn, E., Zawacki-Richter, O., & Kalz, M. (2020). Virtual internationalization in higher education. wbv Media.
de Wit, H., & Hunter, F. (2015). The Future of Internationalization of Higher Education in Europe. International Higher Education, 83, Article 83.
Erlingsson, C. & Brysiewicz, P. (2017). A hands-on guide to doing content analysis. African Journal of Emergency Medicine, 7(3): 93–99.
Knight, J. (2004). Internationalization remodeled: Definition, approaches, and rationales. Journal of Studies in International Education, 8(1), 5–31.
Kobzhev, A., Bilotserkovets, M., Fomenko, T., Gubina, O., Berestok, O., & Shcherbyna, Y. (2020). Measurement and Assessment of Virtual Internationalization Outcomes in Higher Agrarian Education. Postmodern Openings, 11(1Sup1), 78–92.
Liu, W. (2020). The Chinese definition of internationalisation in higher education. Journal of Higher Education Policy and Management. https://doi.org/10.1080/1360080X.2020.1777500
Miles, M.B., Huberman, A.M. & Saldana, J. (1994). Qualitative Data Analysis: A Methods Sourcebook (3rd Edition). New York: SAGE Publications.
Moore, M. G., & Kearsley, G. (2012). Distance education: A systems view of online learning (Vol. 72). Wadsworth Cengage Learning.
Rajagopal, K., Firssova, O., Op de Beeck, I., Van der Stappen, E., Stoyanov, S., Henderikx, P., & Buchem, I. (2020). Learner skills in open virtual mobility. Research in Learning Technology, 28(0).
Saykili, A. (2019). Higher Education in The Digital Age: The Impact of Digital Connective Technologies. Journal of Educational Technology and Online Learning, 1–15.
Woicolesco, V. G., Cassol-Silva, C. C., & Morosini, M. (2022). Internationalization at Home and Virtual: A Sustainable Model for Brazilian Higher Education. Journal of Studies in International Education, 26(2), 222–239.
99. Emerging Researchers' Group (for presentation at Emerging Researchers' Conference)
Paper
Teacher Characteristics and Student Math Achievement the Case of Saudi Arabia
Ahmad Abotalib
University of Glasgow, United Kingdom
Presenting Author: Abotalib, Ahmad
Teacher effectiveness has been one of the most common topics in education for several decades. There has been a noticeable increase in studies examining teacher effectiveness during the past 20 years. One way to examine teacher effectiveness is by investigating the correlation between teacher characteristics as independent variables and student performance in standardised tests as a dependent variable. This study aims to add to existing knowledge by exploring the relationship between teacher characteristics and student math achievement in Saudi Arabia. This study examines teacher characteristics including gender, age, formal education, degree major, experience, and professional development. TIMSS 2019 is the primary data source for this study. The strategy for examining the correlation between teacher characteristics and student achievement in Saudi Arabia relies on including several student and school controls by utilising the Ordinary Least Squared (OLS) to explore this relationship for fourth and eighth-grade students. The findings of this study show that, on average, students with female teachers performed better than those with male teachers in both grades. Also, teachers' age and professional development are positively and significantly correlated with student math achievement in both grades. In addition, teacher major is positively and significantly associated with student achievement only in 4th grade. The estimations for teacher experience were very close to zero in both grades. Furthermore, it was not applicable to examine teacher formal education due to the small sample size. One explicit limitation arises due to missing data, which could bias the results of this study. One way to deal with it is to rerun the model after excluding variables that present high missing values. This model presents the same results. These findings are relevant to education policy discussion since teachers are hired and paid based on these characteristics.
Methodology, Methods, Research Instruments or Sources UsedThe primary data source is the Trends in International Mathematics and Science Studies (TIMSS 2019) dataset. Since 1995, TIMSS has been conducted in several countries every four years, which makes TIMSS 2019 the seventh version of TIMSS. In Saudi Arabia, five types of questionnaires were administered: home, school, teacher, student, and curriculum questionnaires. TIMSS uses stringent school and classroom sampling methodologies to estimate student achievement accurately. They use a two-stage random sample design, with a first stage of selecting a sample of schools and a second stage of choosing one or more intact classes from each sampled school (Martin et al., 2020). As part of the first stage, each sampled school is pre-assigned two substitute schools.
All fourth and eighth-grade students in Saudi Arabia are the target populations for TIMSS 2019. Before identifying the sample, TIMSS divides schools in Saudi Arabia based on gender (boys & girls) and school type (public, private & international). There are two types of exclusions from the sample: at the school level and within schools. Exclusion at the school level comprises very small schools, special needs schools, non-Arabic or non-English speaking schools, and schools in three different cities: Jizan, Najran, and a portion of Asir. Within schools, students with intellectual or functional disabilities and non-native language speakers were excluded from the sample. The final sample includes 5,453 students from 220 schools in fourth grade and 5,680 students from 206 schools in eighth grade.
TIMSS 2019 uses five plausible values to estimate the performance of students. These five plausible values are "random draws from a conditional normal distribution." (Martin et al., 2020, p. 546). For this study, the average score of the five plausible values will be used as an indicator of student performance. TIMSS 2019 includes student variables such as gender, birth location, age, attending pre-primary education, parents' level of education, number of books at home, absence, home support, and extra lessons. Also, TIMSS 2019 provides several school variables such as class size, socio-economic status, area of location, degree of teacher absenteeism, principals' experience at the same school, and principals' level of education.
Following the model used by Hanushek & Luque (2003), I will use Ordinary Least Square (OLS) to examine the correlation between teacher characteristics and student math achievement in fourth and eighth grades in Saudi Arabia. The model is as follows:
Y_ij^PV=β_0^PV+β_1^PV 〖stu〗_ij^PV+β_2^PV 〖tea〗_ij^PV+β_3^PV 〖sch〗_ij^PV+ε_ij^PV
Conclusions, Expected Outcomes or FindingsThis study explores the relationship between several teacher characteristics and standardised student achievement in mathematics in Saudi Arabia. TIMSS 2019 is secondary data that is used to examine this relationship. The strategy of this study is to include as many control variables as possible. Including several student variables reduce the bias caused by omitted variables (Clotfelter et al., 2006). In addition, it eliminates the bias that might occur because of using a cross-sectional dataset since prior student achievement is unavailable. It is essential to notice that the level of the model used in this study is inferior to the value-added model that includes data on prior student achievement (Hanushek & Luque, 2003). The findings of this study show some variations in student achievement that could be attributed to teacher characteristics. One explanation for female superiority over male students could be attributed to the same-gender effect since education in Saudi Arabia is segregated by gender. Some studies show that female students perform better when female teachers teach them (see Paredes, 2014 & Lee et al., 2019). Also, the small estimations for experience might be due to measuring experience as a continuous variable (linear relationship). The literature shows that examining the non-linear relationship between experience and student achievement produces higher estimations than a linear relationship (e.g., Ladd & Sorensen, 2017; Canales & Maldonado, 2018; Bhai & Horoi, 2019; Toropova et al., 2019). Due to the tendency of the Ministry of Education in Saudi Arabia during the past years to raise the level of education by raising the academic degree for teachers (bachelor), the diversity of teachers' educational degree has been confined to a certain degree. Therefore, testing the relationship between teacher formal education and student achievement was not feasible.
ReferencesBhai, M., & Horoi, I. (2019). Teacher characteristics and academic achievement. Applied Economics, 51(44), 4781-4799.
Canales, A., & Maldonado, L. (2018). Teacher quality and student achievement in Chile: Linking teachers' contribution and observable characteristics. International Journal of Educational Development, 60, 33-50.
Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2006). Teacher-student matching and the assessment of teacher effectiveness. Journal of Human Resources, 41(4), 778-820.
Hanushek, E. A., & Luque, J. A. (2003). Efficiency and equity in schools around the world. Economics of Education Review, 22(5), 481-502.
Ladd, H. F., & Sorensen, L. C. (2017). Returns to teacher experience: Student achievement and motivation in middle school. Education Finance and Policy, 12(2), 241-279.
Martin, M. O., von Davier, M., & Mullis, I. V. (2020). Methods and Procedures: TIMSS 2019 Technical Report. International Association for the Evaluation of Educational Achievement.
Toropova, A., Johansson, S., & Myrberg, E. (2019). The role of teacher characteristics for student achievement in mathematics and student perceptions of instructional quality. Education Inquiry, 10(4), 275-299.
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